Optimization of Carbon Nanotube Dispersions in ... - ACS Publications

This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.

Article Cite This: ACS Omega 2019, 4, 849−859

http://pubs.acs.org/journal/acsodf

Optimization of Carbon Nanotube Dispersions in Water Using Response Surface Methodology Qammer Zaib†,‡ and Farrukh Ahmad*,† †

Department of Civil Infrastructure and Environmental Engineering, Khalifa University of Science and Technology, Masdar City Campus, P.O. Box 54224, Abu Dhabi 127788, UAE ‡ Department of Civil and Environmental Engineering, University of Ulsan, 93 Daehakro, Ulsan 680-749, South Korea

ACS Omega 2019.4:849-859. Downloaded from pubs.acs.org by 109.94.222.202 on 01/11/19. For personal use only.

S Supporting Information *

ABSTRACT: The aim of this work was to demonstrate an optimization methodology to reliably obtain stable macrodispersions (i.e., for ≥24 h) of carbon nanotubes in water using sonication. Response surface methodology (RSM) was utilized to assess and optimize the sonication parameters for the process. The studied input parameters were (i) sonication time (duration), (ii) amplitude (of vibration), and (iii) pulseon/off (duration) of the sonicator. The analyzed responses were mean diameter and size distribution of multiwalled carbon nanotube (MWNT) aggregates in water, which were measured by the dynamic light scattering technique. A semiempirical model was developed and statistically tested to estimate the magnitude of sonicator parameters required to obtain specified MWNT macrodispersions (i.e., aggregates’ mean diameter and distribution) in water. The results showed that MWNT aggregates of 2 ± 0.5 μm can be obtained by optimizing sonicator parameters to a sonication time of 89 s, amplitude of 144 μm, and pulse-on/off cycle of 44/30 s. These process settings for 100 mg/L MWNTs in a 30 mL aliquot of deionized water would consume 863 J/mL of sonication energy. Contrary to the popular belief, “sonication time” and/or “sonication energy input” were not found to be proportional to the degree of dispersion of MWNTs in water. This might be the reason for the frequent disparity and nonreproducibility of sonication results reported in scientific literature, especially for dispersing nanomaterials in a number of different systems. The amplitude of vibration was noted to be the most sensitive parameter affecting MWNT aggregates’ diameter and distribution in water. The characterization of MWNTs was performed using electron microscopy, surface area analyzer, thermogravimetric analyzer, and zeta potential analyzer. This study can be helpful in evaluating sonication dispersion of particulate matter in other incompressible fluids such as graphene dispersion in organic solvents. together in an aggregate. 11−15 To disperse them, an appropriate amount of external energy is required to overcome this high amount of binding energy. Continual efforts have been underway to obtain stable dispersions of carbon nanotubes since their discovery. The methods used to disperse carbon nanotubes can be broadly categorized into either chemical or mechanical methods. For chemical methods, researchers have approached the problem by mostly varying (i) solvents,16 (ii) solvent compositions,14,17 and (iii) solvent additives such as surfactants and macromolecules.6,9,10,12,18,19 Mechanical methods such as shear mixing, ball milling, and melt blending are sometimes used;20 however, ultrasonication (aka sonication) remains the most popular mechanical method to disperse carbon nanotubes.6,9,12,21,22 Unless otherwise required, water is a natural choice for solvent owing to its universal availability, low cost, inherent

1. INTRODUCTION Carbon nanotubes form suspensions in water but their dispersion state changes over time making it difficult to quantify their degree of dispersion.1 To address this problem, NIST and NASA defined the terms “macrodispersion” and “nanodispersion” for carbon nanotube suspensions.2 Macrodispersion represents dispersed aggregates of carbon nanotubes, while nanodispersion refers to ones consisting of individual carbon nanotubes.3 Both types of dispersions have their own significance and applications; however, this work is focused on macrodispersions of carbon nanotubes. Carbon nanotubes are known to remarkably improve the electrical, mechanical, and photocatalytic properties of composites.4−6 These exceptional benefits of carbon nanotubes can be harnessed only if they are reasonably dispersed (in the medium) during the synthesis of composites.6−9 However, strong interaction forces between carbon nanotubes cause their agglomeration into aggregates, thereby limiting their dispersion in most solvents.6,10 On average, 500−950 eV binding energy per micrometer of carbon nanotube length holds them © 2019 American Chemical Society

Received: October 26, 2018 Accepted: December 26, 2018 Published: January 10, 2019 849

DOI: 10.1021/acsomega.8b02965 ACS Omega 2019, 4, 849−859

ACS Omega

Article

Billerica, MA, USA). Fresh deionized water, with an average resistivity of 18.2 MΩ·cm, was used in all experiments. 2.2. Cleaning Procedure for MWNTs. The as-received MWNTs were treated with hydrochloric acid, using previously established protocol27 summarized in Figure S1. Briefly, 1 g of MWNTs was added to 500 mL of Pyrex glass container followed by the addition of 200 mL of 10 M hydrochloric acid. The suspension was stirred for 6 h using a magnetic stirrer. Three hundred milliliters of deionized water were added, and the suspension was filtered through binder-free glass microfiber filter, Whatman GF/F (GE Healthcare Life Sciences, UK), using a vacuum filtration apparatus. At least seven washes of the MWNT cake, hence obtained, were performed (using deionized water) to remove residual hydrochloric acid. Finally, the MWNT cake was dried to constant mass in the oven at 70 °C to remove moisture and vaporize away the hydrochloric acid, if any. The cake was ground to powder using a glass rod in the prewashed borosilicate beaker and stored in an air tight container until further use. 2.3. Characterization of MWNTs and MWNT Dispersions. The structure of MWNTs was examined by scanning electron microscopy (SEM) using an FEI Quanta FEG 250 SEM from FEI Co. (Hillsboro, OR, USA). The equipment was operated at ∼20 keV. Prior to imaging, the sample surface was coated with 500 °C exhibits the decomposition of amorphous carbon.32 Therefore, it can be seen that the MWNTs carry adsorbed water (0.6% mass loss until 100 °C), contain some functionalities (3.5% mass loss until 500 °C), and oxidize at 600 °C. The 3.5% mass loss (until 500 °C) cannot be wholly attributed to functional groups, because some might be due to amorphous carbon. Lehman et al.33 reported the oxidation temperature of single-walled carbon nanotubes at 350 °C. MWNTs, in this study, were prepared by catalytic vapor deposition, where metal catalyst acts as a precursor for the growth of carbon nanotubes. In this process, the probability of single-walled carbon nanotube formation cannot be completely ignored. In the case of our material, however, there was only a 0.5% loss in mass from 350 to 500 °C, indicating very few single-walled carbon nanotubes, if any. Figure 1c displays the change in zeta potential of MWNTs with the change in surrounding aqueous pH. Zeta potential measurements are indicative of ionically stabilized colloid systems, that is, the stability of colloidal system increases with the increase of zeta potential of suspended particles above ±25 mV.34 Zeta potential can also be explained in terms of two layers of liquid surrounding the particle: (i) the stern layer and (ii) the diffuse layer. The stern layer exists as an inner layer where the ions are strongly bound to the particle, whereas the diffuse layer is the outer layer where ions are less firmly bound to the particles. The potential at this outer boundary is measured through electrophoretic mobility using the Smoluchowski equation to estimate zeta potential.22 The point of zero charge of MWNTs was calculated, by interpolating the measured zeta potential values at pH 3, 4.5, 6, 7.5, 9, and 10.5, to be at pH 6.6. Adsorption, dispersion, catalytic activity, functionalization, and toxicity are some of the characteristics that are directly dependent upon the surface architecture of a material. Many uncommon properties of MWNTs are attributed to their unique three-dimensional surface structure.33,35 Therefore, understanding the surface area helps in elucidating MWNT interaction with other materials. In an individual MWNT, the spaces between concentric cylinders of graphene are 0.34 nm36 which are too narrow to accommodate nitrogen gas molecule

(1)

where dh is the hydrodynamic diameter (m) of an equivalent sphere of MWNT aggregates, kB is the Boltzmann constant (1.3807 × 10−23 J K−1), T is the absolute temperature (K), and η is the dynamic viscosity of water (0.009 kg m−1 s−1) under the experimental conditions. The standard deviation of MWNT aggregates, representing the distribution of MWNTs, was calculated using MS Excel software. To obtain the standard deviation, at least 10 runs of five cycles each were performed on each sample. 2.5. Experimental Design. RSM was applied to identify the effect of various sonicator parameters on MWNT dispersion. RSM is a collection of statistical and mathematical techniques helpful for developing, improving, and optimizing processes.29,30 The use of RSM in dispersing MWNTs reduces the process variability and requires fewer resources. Central composite design (CCD), a form of RSM, was used with four variables at five levels. To understand the relationship between the sonicator’s operating parameters and the dispersion of MWNTs in water, four independent variables (sonication time, the amplitude of vibration, pulse-on duration, and pulse-off duration) were selected. The effects of these variables on two responses, namely, MWNT aggregate size and aggregate distribution, were examined. The details of actual variables and their corresponding dimensionless factors, at studied levels, can be found in Table 1. Alpha (α) is the coded level of Table 1. Variables and Levels of Chosen Factors for Central Composite Design coded levels factor

variable

units

−α

−1

0

1

α

A B C D

time amplitude pulse-on pulse-off

s μm s s

2 36 2 0

31 72 16 15

60 108 30 30

89 144 44 45

118 180 58 60

the axial point and “1” is for a factorial point from the center “0”. The coding was performed to normalize the effects of factors on responses, and the coded levels were obtained by subtracting the actual values of the variables from their value at the central point and dividing by the step chance value. Full factorial design requires 54 = 625 experiments, however (as shown in Table 2), RSM reduced the required experiments to 30 only: 8 at axial, 16 at factorial, and 6 at center points of deign. Table 2 provides the detailed description of actual experimental conditions recommended by RSM and their position in the design space. It should be noted that (in Table 2) the values of sonicator’s input variable [A: time (s)] and its corresponding total sonication energy (J) are based on unit volume (1 mL) of dispersions. In practice, 30 mL aliquots were processed. Therefore, the sonication time [A: time (s)] (and 851


ACS Omega

Article

Table 2. Experimental Design Matrix Based on a Central Composite Design Using Full Factorial sonicator input variables (factors)

dispersion of MWNTs (responses)

run

space type

A: time (s)

B: amplitude (μm)

C: pulse-on (s)

D: pulse-off (s)

total sonication energy (J)

MWNTs mean diameter (nm)

standard deviation of MWNTs aggregates [nm]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

axial center center axial factorial center factorial factorial axial axial factorial factorial factorial factorial factorial factorial axial axial axial center factorial center factorial factorial factorial center axial factorial factorial factorial

60 60 60 118 89 60 89 31 60 60 31 89 31 89 31 31 60 2 60 60 89 60 89 31 89 60 60 31 31 89

108 108 108 108 72 108 144 144 108 180 72 72 72 144 144 72 36 108 108 108 72 108 144 144 72 108 108 144 72 144

30 30 30 30 44 30 44 44 2 30 16 16 16 44 44 44 30 30 58 30 44 30 16 16 16 30 30 16 44 16

60 30 30 30 15 30 45 15 30 30 15 45 45 15 45 45 30 30 30 30 45 30 45 45 15 30 0 15 15 15

341 348 341 668 259 345 872 304 236 858 85 244 86 872 304 88 35 11 349 344 259 340 861 300 248 341 342 300 87 861

2864 3773 1999 3132 10 411 2143 2427 3431 2974 1879 4937 2830 1674 2203 3108 3978 6022 1318 4638 2489 3023 3958 2000 4151 5464 4090 4402 4163 5831 2639

672 1188 655 1074 509 754 2679 1944 634 3443 930 697 906 1573 2821

2667 474 2317 2520 561 3658 2934 2955 1793 4569 502

Figure 1. Characterization of MWNTs used in this study: (a) scanning electron micrograph, (b) TGA, (c) zeta potential, (d) adsorption/ desorption of nitrogen gas, and (e) pore size distribution.

whose kinetic diameter is approximately 0.36 nm.37 However, MWNTs tend to aggregate, in water and air, at ordinary conditions. The typical MWNT aggregate carries four distinct sites for nitrogen adsorption: (i) external surface area; (ii) groove area; (iii) interstitial area; and (iv) inner pores.35,38,39

The overall magnitude of these multiple nitrogen adsorption sites depends upon a number of factors such as synthesis procedure, purification methods, and chemical and physical modifications.40 These factors can be held responsible for large differences (up to 75×) in reported specific surface areas of 852


ACS Omega

Article

Figure 2. (a) Photographic images of MWNT macrodispersions after sonication and one-day settling time. The number corresponds to experimental runs listed in Table 2. (b) Correlation between mean diameter and size distribution of MWNT aggregates as a function of sonication energy supplied. Correlation between the experimental and predicted values of MWNT aggregates’ (c) mean diameter and (d) size distribution in water.

MWNTs from 22.4 to 1670 m2/g.41,42 The curves of the nitrogen adsorption/desorption isotherms of MWNTs (Figure 1d) are convex and comparable to the reversible type III isotherm under IUPAC classification, which exhibit weak attractive adsorbate−adsorbent interactions.43 The type III isotherm is characterized by heats of adsorption less than the adsorbate heat of liquefaction, whereby adsorption proceeds with the adsorbate’s interaction with an adsorbed layer (i.e., adsorbate−adsorbate interaction) that is greater than that of the adsorbent’s surface (adsorbate−adsorbent interaction).43−46 This kind of isotherm is reported for nitrogen adsorption on the basal plane of graphite.45 Therefore, from the structure of MWNT aggregate, it can be assumed that most of the nitrogen adsorption took place on the external surface area of MWNTs. The desorption of nitrogen was employed to calculate BET surface area at low-pressure ranges.46,47 The recommended range of P/Po from 0.35 to 0.05 was utilized to calculate BET surface area.47 MWNTs, in this study, carried a specific surface area of 440.7 m2/g. Pore size distribution was investigated using density functional theory (DFT). Classical macroscopic theories such as the BJH method, DR approach, and SF semiempirical treatment all fail to provide a realistic description of the filling of micropores and narrow mesopores, leading to an underestimation of pore sizes. To achieve a reasonable description of pore size distribution, DFT was applied to understand the sorption and phase behavior of fluids in narrow pores at a molecular level.48 Figure 1e shows the pore size distribution of MWNTs. It can be seen that the majority of the pores in MWNTs are mesopores having a diameter between 6 and 20 nm. 3.2. MWNTs Dispersion in Water. Macrodispersions of MWNTs were prepared using sonication energy. Table 2 describes the details of experimental runs (from column 1 to 9): (i) number of experiments, (ii) position of each experiment in the CCD space, (ii) time duration of sonication, (iv)

amplitude of vibration, (v) duration of pulse-on, (vi) duration of pulse-off, (vii) resulting sonication energy from time, amplitude, and pulse-on/off, (viii) average diameter of MWNT aggregates, and (ix) size distribution of MWNT aggregates in dispersion. The values of input variables A−D were suggested by RSM and outputs were calculated by (i) calibrating the sonicator (sonication energy)22,49 and (ii) analyzing the MWNT dispersions by DLS (the responses average aggregate size and distribution). Figure 2a presents the photographic images of MWNTs, sonicated at the different conditions specified in Table 2. These images can be visually evaluated in comparison to the factors and responses (Table 2). Therefore, before proceeding further, the data were tested for their smoothness (precision) by correlating distribution of MWNT aggregates with their mean sizes at different sonication energies. Figure 2b, hence obtained, gives a reasonable correlation (R2 = 0.84) between the diameter and standard deviation of MWNT aggregates. Also, the high sonication energy dispersions (red dots, see key within Figure 2b) cluster in the region where MWNTs diameter is small and distribution is narrow. This improves confidence in the validity of the dataset. From Figure 2a,b, one can pick up the visually desirable dispersion state of MWNTs and estimate its aggregate size corresponding to the required sonication energy. A general conclusion drawn from Figure 2a,b was that the average aggregate size of ∼2−6 ± 0.5 μm can be achieved by sonicating MWNTs in water at energies below 1 kJ per mL water, provided that the working volume is 30 mL and concentration of MWNTs is 100 mg/L. Figure 2b (along with Figure S2 in the Supporting Information) can be helpful for a rough estimation of MWNT macrodispersions. The average diameter and standard deviation (distribution around mean diameter) were measured to estimate the aggregation state of MWNTs in water. The diameter and distribution data of MWNT aggregates in Figures 2b and S2 can be plotted against input variables (variables A−D in Table 2) to find their mutual 853


ACS Omega

Article

relationship, if any. Therefore, the effect of each factor on MWNT dispersion was studied by plotting the MWNT aggregates’ mean diameter and size distribution against sonication time, amplitude, pulse-on duration, and pulse-off duration (Figures S3−S6). The objective, there, was to find out whether any correlation exists between the sonicator’s operating parameters and dispersion of MWNTs in water. Figure S3 shows the absolute absence of correlation between time of sonication and MWNT aggregates’ diameter as well as its distribution. This raises a serious concern for researchers reporting carbon nanotubes dispersion in water in terms of sonication time. Similarly, amplitude also failed to explain MWNT dispersion (Figure S4), as did the pulse-on (Figure S5) and pulse-off (Figure S6) durations. Pulse-off duration was studied owing to its indirect effect on dispersion by impacting the temperature of the system being sonicated. This effect will become more prominent in subsequent sections (Pareto analysis). Because the individual sonicator’s parameters could not explain the macrodispersion of MWNTs, their combined effect was plotted. Figure S7 shows the plot of sonication energy versus dispersion of MWNTs. The increase in the quantity of sonication energy was expected to enhance the dispersion of MWNTs; however, no significant correlation between sonication energy and MWNTs dispersion in water was observed. Therefore, contrary to the popular belief, it was hypothesized that “the sonication energy is not proportional to the quality of MWNTs aqueous dispersions”.50 3.3. Model Fitting and Analysis. Because the individual sonicator operational parameters failed to describe the dispersion of MWNTs in water, experimental results were utilized to develop semiempirical expressions capable of expressing MWNT dispersions in water. Equations 2 and 3 represent the relationship between sonicator’s parameters and mean diameter of MWNT aggregates in coded and actual terms of studied factors, respectively. Similarly, eqs 4 and 5 were developed for MWNT aggregates’ size distribution in water, in terms of standard deviation from the mean aggregate diameter. The coded equations (eqs 2 and 4) are useful for comparing the relative impact of factors while equations with actual factors (eqs 3 and 5) are suitable for predicting responses. Equations 3 and 5 cannot be used to compare the relative impact of sonicator’s variables on dispersion within each equation because the terms in these relationships are corrected to accommodate for units.

MWNTs distribution [st dev] = 1675.06 + 143.88 × A − 2011.62 × B + 369.34 × C − 1088.84 × D − 1230.03 × AB + 207.21 × AC − 880.98 × AD + 1431.01 × BD + 1311.4 × B2

MWNTs distribution [st dev] = 18825.7 + 177.65 × time + −283.25 × amplitude − 4.240 × pulse‐on + −237.28 × pulse‐off − 1.18 × time × amplitude + 0.51 × time × pulse‐on − 02.03 × time × pulse‐off + 2.65 × amplitude × pulse‐off + 1.01 × amplitude 2 (5)

Tables S1 and S2 display the results obtained from analysis of variance (ANOVA) of the quadratic models developed for estimation of MWNT aggregates’ mean diameter and size distribution, respectively. The F-value of 8.32 and 12.7 imply that the models are significant.51 There is only a 0.01% chance that an F-value this large could occur because of noise. Values of probability > F and less than 0.05 indicate that model terms are significant. In the case of MWNT aggregates’ mean diameter, there are five significant model terms (B, D, AB, BC, and BD), whereas in the case of MWNT aggregates’ size distribution six significant model terms were identified (B, D, AB, AD, BD, and B2). The amplitude (B), pulse-off (D), sonication time × amplitude (AB), and amplitude × pulse-off (BD) are common significant model terms in the two models. In addition to these four terms, amplitude × pulse-on duration (BC) term is significant for MWNT aggregates’ mean diameter only. However, sonication time × pulse-off (AD) and amplitude2 (B2) terms are significant for the MWNT aggregates’ size distribution only. This implies that along with amplitude of vibration, the sonicator’s pulse-on duration is influential to the mean aggregate diameter while the size distribution is sensitive towards pulse-off duration. This difference might be due to the system temperature regulation for MWNT dispersion, which is often recommended in the literature.8,50,52 This might also be the reason for the failure of sonication energy to describe dispersions (Figure S7) because sonication energy is unable to account for the “duration” of pulse-off. The “lack of fit values” for the two models were not found to be significant, hence providing further evidence for their validity. There were 42 and 18.9% chances for the occurrence of lack of fit due to noise for MWNT aggregate mean diameter and its size distribution, respectively. When it came to regression of the models, a reasonable agreement (0.185 < 0.2) between predicted and adjusted R2 was observed for MWNT diameter but a little less than desired (0.27) for MWNTs distribution. This can be attributed to the block effect and/or outliers. The predictability of the model could be slightly improved (5−10%) by transforming the response to square root function but the significant variables as well as the shape of response surface will be minimally altered. Therefore, the response was not transformed. However, “adequate precision” value (i.e., signal-to-noise ratio) for both models was at least three times higher than the required value of 4, allowing these models to navigate throughout the design space. To conclude, ANOVA results supported the model. All empirical models require confirmation runs even after statistical approval.53 Figure 2c,d correlate the experimental and predicted (by model) values for MWNT aggregates’ mean

MWNTs mean diameter = 3598.39 + 139.58 × A + −929.73 × B + 411.78 × C − 790.21 × D + −680.76 × AB − 632.79 × BC + 899.11 × BD (2)

MWNTs mean diameter = 3898.18 + 75.237 × time + 1.01 × amplitude + 165.01 × pulse‐on − 232.5 × pulse‐off − 0.65 × time × amplitude + −1.256 × amplitude × pulse‐on + 1.67 × amplitude × pulse‐off

(4)

(3) 854


ACS Omega

Article

Figure 3. Pareto graphic analysis for the contribution of sonicator’s operational parameters influencing (a) mean diameter and (b) distribution of MWNT aggregates.

Figure 4. (a) Perturbation plot for the impact of variables on mean diameter of MWNT aggregates in water. The contours for the combined effect of (b) sonication time and amplitude, (c) amplitude and pulse-off duration, and (d) amplitude and pulse-on duration, on the mean diameter of MWNT aggregates in water. (e) Perturbation plot for the impact of variables on the size distribution of MWNT aggregates in water. The contour plots for the combined effect of (f) sonication time and amplitude, (g) amplitude and pulse-off duration, (h) sonication time and pulse-on duration, and (i) sonication time and pulse-off duration on the size distribution of MWNT aggregates in water.

often recommended. From Tables S1 and S2, the adjusted R2 values of 0.64 and 0.78 can be evidenced, which approve the two models to be statistically significant. 3.4. Effects of Sonication Variables on MWNTs Dispersion in Water. After statistical analysis and experimental validation, the terms in the models were assessed for their role in producing the responses. Pareto analysis is a useful tool to quantify the relative contribution of terms in a model toward the overall response. Figure 3 presents the results obtained from Pareto analysis. The magnitude of the terms, in the model developed for MWNT aggregates’ mean diameter,

diameter (Figure 2c) and size distribution (Figure 2d). The plots help to assess the distribution of actual experimental observations in comparison to those predicted by the models. The experimental data were found to be in good correlation with the predicted values. The correlations were statistically significant; however, regression value for MWNT aggregates’ diameter (Figure 2c) was lower than that for size distribution (Figure 2d). It should be noted that R2 is not the most reliable test for goodness-of-fit for multiple linear regression analysis because it can be affected (improved) by adding the statistically insignificant terms.51,53 Therefore, adjusted R2 is 855


ACS Omega

Article

an aqueous system. The detailed criteria set to achieve this goal can be seen in Table 3. Briefly, the entire range of sonicator’s

can be arranged in order of decreasing importance as follows: amplitude > amplitude × pulse-off > pulse-off > time × amplitude > amplitude × pulse-on > pulse-on > time. The share of sonication parameters in MWNT aggregates’ size distribution from highest to lowest importance was amplitude > amplitude × pulse-off > amplitude2 > time × amplitude > pulse-off > time × pulse-off > pulse-on > time × pulse-on > time. The most obvious observation made from these charts was the highest impact of sonication amplitude on both responses (MWNT aggregates’ diameter and size distribution). It also revealed the significance of “pulse-off”, which is often an ignored parameter in the literature for sonication. Interestingly, total sonication time, which is the most reported parameter to describe sonication, carries the minimum weightage in both models. Its contribution reaches above one percent only when it interacts with other parameters, such as amplitude and pulse mode. 3.5. Analysis of Contours and Response Surface Plots. The perturbation plot presented in Figure 4a shows the impact of individual factors on the response. The plot is generated by keeping all variables constant except for one, resulting in an estimation of an uninterrupted impact of variables. The slope of the line, hence generated, is proportional to the quantity of impact on the response. A positive slope corresponds to an increase in the value of the response and vice versa. Figure 4a depicts an increase in diameter of the MWNT aggregates upon increasing the time of sonication and pulse-on duration and, conversely, a decrease in aggregate diameter upon increasing amplitude and pulse-off duration. Hence, when it comes to the effect posed by individual operational parameters of the sonicator, amplitude of vibration causes the highest desired impact (i.e., steepest negative slope) on the mean diameter of MWNT aggregates. To note, the negative slope means a reduction in MWNT aggregate mean diameter with an increase in amplitude. A similar trend of sonicator’s operational parameters is observed for MWNT aggregates’ size distribution (Figure 4e), except that the effect of amplitude is higher when compared with that of MWNT aggregate’s mean diameter. In the absence of interaction of factors, perturbation plots reveal that MWNT aggregates were reduced in diameter and limited to a narrower size distribution by an increase in amplitude, increase in pulseoff duration, decrease in total time of sonication, and decrease in pulse-on duration. Perturbation plots are good tools for exploiting variables individually, however, they cannot describe the interaction between factors and their effect on responses.53 There is also a significant contribution from interactions of factors as revealed by Pareto analysis. Figure 4b shows the combined impact of sonication time and amplitude on the mean diameter of MWNT aggregates. It displays the region where the combined impact of [sonication time + amplitude] produces conditions to generate MWNT aggregates of a narrowly defined diameter range. This plot can be helpful for estimating the required amount of sonication time and amplitude to obtain the desired range of MWNT aggregates provided that pulse-on/off is kept constant at 30/30 s. Similarly, Figure 4c represents the combined effects of [amplitude + pulse-off], and Figure 4d shows combined impacts of [amplitude + pulse-on] on MWNT aggregates. Figure 4f−i evaluates combined effects of factors on MWNT aggregates’ size distribution. 3.6. Optimization of Sonication Energy. The ultimate objective of this study was to obtain well-dispersed MWNTs in

Table 3. Criteria for Optimization of Sonicator’s Parameters to Obtain Desirable Dispersion of MWNTs in Water constraint

units

lower limit

upper limit

goal

time amplitude pulse-on pulse-off sonication energy MWNTs mean dia MWNTs distribution

s μm s s J nm nm

31 72 16 15 11.3 1318 474

89 144 44 45 871.8 10 411 13 106

entire range entire range entire range entire range entire range minimize minimize

parameters was scanned to obtain conditions at which a small MWNT mean aggregate size with a narrow size distribution could be achieved. Figure S8 shows these ramps of defined desirability. The flat ramps represent uniform desirability, whereas the negative slope of the ramps represent the minimization of numerical value (MWNT aggregate diameter and MWNT distribution) defined in the optimization criteria. The factors and responses are represented by blue and red dots, respectively. As shown in Figure S8, 96.7% of the set criteria can be achieved by sonicating MWNTs for 89 s, keeping the amplitude at 144 μm, and pulse-on/off cycle of 44/30 s. Also, on the basis of these settings, 863 J sonication energy will be consumed (per mL water) during this process. The two-dimensional contour plot in Figure 5a displays the “Flag” where maximum desirability can be achieved with respect to the optimal sonicator settings listed in Table 3. Figure 5b−d represents the corresponding sonication energies, MWNT aggregates’ mean diameter, and MWNT aggregates’ size distribution, respectively, for the optimal settings (Table 3 and Figure 5a).

4. CONCLUSIONS The following conclusions were derived from this study: 1. Sonication is a useful tool to disperse MWNTs in water, provided that it is used with appropriate understanding. 2. Dispersion of MWNTs in water depends on amplitude, sonication time, and the pulse mode of the sonicator. 3. RSM can be utilized to understand and optimize the MWNT macrodispersions in water. 4. MWNT dispersion in water by sonication is not the function of individual sonication parameter whether it be sonication time, amplitude, or pulse mode. 5. MWNT dispersion in water is not proportional to the magnitude of sonication energy provided to the system. 6. Total time of sonication, which corresponds to magnitude of sonication energy, cannot alone describe the dispersion of MWNTs in water. 7. The amplitude of sonicator’s vibration collectively (alone and in combination with other parameters) contributes over 75% toward dispersing MWNTs as portended by Pareto analysis. 8. “Pulse-off” duration of sonicator plays an important role in the dispersion of MWNTs in water. 9. In our [MWNT−water-sonication] system, the desired diameter (2 ± 0.5 μm) and distribution of MWNT aggregates were obtained by optimizing sonicator’s parameters to sonication time of 89 s, amplitude of 144 μm, and pulse-on/off cycle of 44/30 s. During this 856


ACS Omega

Article

Figure 5. (a) Desirability, (b) its corresponding sonication energy, (c) MWNT aggregates’ mean diameter, and (d) size distribution, obtained at optimized parameters of sonicator’s operation. Optimization criteria are described in Table 3.

process, 863 J/mL sonication energy was expended to disperse 100 mg/L MWNTs in a 30 mL aliquot of deionized water. 10. This optimization process can be adopted (with modification) for other [particle-solvent/aqueous-sonication] systems where appropriate dispersions are required such as synthesis of graphene from graphitediethyl ether-sonication.54

■

■

ASSOCIATED CONTENT

S Supporting Information *

sonication energy received by the system and the dispersion of MWNTs. (a) The mean diameter and (b) size distribution of MWNT aggregates; and desirability ramps for numerical optimization. Red and blue dots represent factors and responses, respectively. The position of red dots on the ramp is indicative of the specific input value where maximum desirability was achieved (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: +971 (02) 8109114 (F.A.).

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsomega.8b02965.

ORCID

Qammer Zaib: 0000-0002-1924-4131 Farrukh Ahmad: 0000-0003-1405-220X

ANOVA results for the response surface (CCD) model developed for MWNT aggregates’ mean diameter; ANOVA results for the response surface (CCD) model developed for MWNT aggregates’ size distribution; acid treatment of as received MWNTs for removing metallic impurities and amorphous carbon; MWNT aggregates’ mean diameter and size distribution for the studied sonication variables in Table 2. The color of data point corresponds to sonication energy provided to the sample (see insert); correlation between sonication time and dispersion of MWNTs in water in terms of (a) mean diameter and (b) size distribution of MWNT aggregates; correlation between the amplitude of sonicator’s vibration and dispersion of MWNTs. (a) The mean diameter and (b) size distribution of MWNT aggregates; correlation between pulse-on duration and dispersion of MWNTs in water. (a) The mean diameter and (b) size distribution of MWNT aggregates; correlation between pulse-off duration and dispersion of MWNTs. (a) The mean diameter and (b) size distribution of MWNT aggregates; correlation between

Funding

Masdar Institute of Science and Technology (grant no. SG2015-000029). Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We gratefully acknowledge the valuable feedback from Dr. Philip M. Gschwend of the Massachusetts Institute of Technology (USA) during the course of this study.

■

REFERENCES

(1) Saleh, N. B.; Pfefferle, L. D.; Elimelech, M. Aggregation kinetics of multiwalled carbon nanotubes in aquatic systems: measurements and environmental implications. Environ. Sci. Technol. 2008, 42, 7963−7969. (2) Kim, S. W.; Kim, T.; Kim, Y. S.; Choi, H. S.; Lim, H. J.; Yang, S. J.; Park, C. R. Surface modifications for the effective dispersion of carbon nanotubes in solvents and polymers. Carbon 2012, 50, 3−33.

857


ACS Omega

Article

(3) Green, M. J. Analysis and measurement of carbon nanotube dispersions: nanodispersion versus macrodispersion. Polym. Int. 2010, 59, 1319−1322. (4) Baughman, R. H.; Zakhidov, A. A.; de Heer, W. A. Carbon nanotubes–the route toward applications. Science 2002, 297, 787− 792. (5) Ahmad, F.; Zaib, Q. Photo-Regenerable Filters Useful for the Removal of Organic Compounds. U.S. US20,150,218,013 A1, 2015. (6) Huang, Y. Y.; Terentjev, E. M. Dispersion of carbon nanotubes: mixing, sonication, stabilization, and composite properties. Polymers 2012, 4, 275−295. (7) Niyogi, S.; Hamon, M. A.; Perea, D. E.; Kang, C. B.; Zhao, B.; Pal, S. K.; Wyant, A. E.; Itkis, M. E.; Haddon, R. C. Ultrasonic dispersions of single-walled carbon nanotubes. J. Phys. Chem. B 2003, 107, 8799−8804. (8) Taurozzi, J. S.; Hackley, V. A.; Wiesner, M. R. Ultrasonic dispersion of nanoparticles for environmental, health and safety assessmentissues and recommendations. Nanotoxicology 2010, 5, 711−729. (9) Yang, K.; Yi, Z.; Jing, Q.; Yue, R.; Jiang, W.; Lin, D. Sonicationassisted dispersion of carbon nanotubes in aqueous solutions of the anionic surfactant SDBS: The role of sonication energy. Chin. Sci. Bull. 2013, 58, 2082−2090. (10) Islam, M. F.; Rojas, E.; Bergey, D. M.; Johnson, A. T.; Yodh, A. G. High weight fraction surfactant solubilization of single-wall carbon nanotubes in water. Nano Lett. 2003, 3, 269−273. (11) O’Connell, M. J.; Boul, P.; Ericson, L. M.; Huffman, C.; Wang, Y.; Haroz, E.; Kuper, C.; Tour, J.; Ausman, K. D.; Smalley, R. E. Reversible water-solubilization of single-walled carbon nanotubes by polymer wrapping. Chem. Phys. Lett. 2001, 342, 265−271. (12) Strano, M. S.; Moore, V. C.; Miller, M. K.; Allen, M. J.; Haroz, E. H.; Kittrell, C.; Hauge, R. H.; Smalley, R. E. The role of surfactant adsorption during ultrasonication in the dispersion of single-walled carbon nanotubes. J. Nanosci. Nanotechnol. 2003, 3, 81−86. (13) Thess, A.; Lee, R.; Nikolaev, P.; Dai, H.; Petit, P.; Robert, J.; Xu, C.; Lee, Y. H.; Kim, S. G.; Rinzler, A. G.; Colbert, D. T.; Scuseria, G. E.; Tomanek, D.; Fischer, J. E.; Smalley, R. E. Crystalline ropes of metallic carbon nanotubes. Science 1996, 273, 483. (14) Sabba, Y.; Thomas, E. L. High-concentration dispersion of single-wall carbon nanotubes. Macromolecules 2004, 37, 4815−4820. (15) Girifalco, L. A.; Hodak, M.; Lee, R. S. Carbon nanotubes, buckyballs, ropes, and a universal graphitic potential. Phys. Rev. B: Condens. Matter Mater. Phys. 2000, 62, 13104−13110. (16) Ausman, K. D.; Piner, R.; Lourie, O.; Ruoff, R. S.; Korobov, M. Organic solvent dispersions of single-walled carbon nanotubes: toward solutions of pristine nanotubes. J. Phys. Chem. B 2000, 104, 8911−8915. (17) Kim, K. K.; Yoon, S.-M.; Choi, J.-Y.; Lee, J.; Kim, B.-K.; Kim, J. M.; Lee, J.-H.; Paik, U.; Park, M. H.; Yang, C. W.; An, K. H.; Chung, Y.; Lee, Y. H. Design of dispersants for the dispersion of carbon nanotubes in an organic solvent. Adv. Funct. Mater. 2007, 17, 1775− 1783. (18) Yu, J.; Grossiord, N.; Koning, C. E.; Loos, J. Controlling the dispersion of multi-wall carbon nanotubes in aqueous surfactant solution. Carbon 2007, 45, 618−623. (19) Krause, B.; Mende, M.; Pötschke, P.; Petzold, G. Dispersability and particle size distribution of CNTs in an aqueous surfactant dispersion as a function of ultrasonic treatment time. Carbon 2010, 48, 2746−2754. (20) Hilding, J.; Grulke, E. A.; Zhang, Z. G.; Lockwood, F. Dispersion of carbon nanotubes in liquids. J. Dispersion Sci. Technol. 20032007, 24, 1−41. (21) Koshio, A.; Yudasaka, M.; Zhang, M.; Iijima, S. A simple way to chemically react single-wall carbon nanotubes with organic materials using ultrasonication. Nano Lett. 2001, 1, 361−363. (22) Zaib, Q.; Khan, I. A.; Yoon, Y.; Flora, J. R. V.; Park, Y.-G.; Saleh, N. B. Ultrasonication study for suspending single-walled carbon nanotubes in water. J. Nanosci. Nanotechnol. 2012, 12, 3909−3917.

(23) Walther, J. H.; Jaffe, R.; Halicioglu, T.; Koumoutsakos, P. Carbon nanotubes in water: structural characteristics and energetics. J. Phys. Chem. B 2001, 105, 9980−9987. (24) Suslick, K. S. Sonochemistry. science 1990, 247, 1439−1445. (25) Adewuyi, Y. G. Sonochemistry: environmental science and engineering applications. Ind. Eng. Chem. Res. 2001, 40, 4681−4715. (26) Mason, T. J.; Lorimer, J. P. Applied Sonochemistry. The Uses of Power Ultrasound in Chemistry and Processing; Wiley-VCH, 2002; pp 1−48. (27) Datsyuk, V.; Kalyva, M.; Papagelis, K.; Parthenios, J.; Tasis, D.; Siokou, A.; Kallitsis, I.; Galiotis, C. Chemical oxidation of multiwalled carbon nanotubes. Carbon 2008, 46, 833−840. (28) Smith, B.; Wepasnick, K.; Schrote, K. E.; Bertele, A. R.; Ball, W. P.; O’Melia, C.; Fairbrother, D. H. Colloidal properties of aqueous suspensions of acid-treated, multi-walled carbon nanotubes. Environ. Sci. Technol. 2008, 43, 819−825. (29) Arabi, M.; Ostovan, A.; Ghaedi, M.; Purkait, M. K. Novel strategy for synthesis of magnetic dummy molecularly imprinted nanoparticles based on functionalized silica as an efficient sorbent for the determination of acrylamide in potato chips: optimization by experimental design methodology. Talanta 2016, 154, 526−532. (30) Ostovan, A.; Ghaedi, M.; Arabi, M.; Yang, Q.; Li, J.; Chen, L. Hydrophilic multitemplate molecularly imprinted biopolymers based on a green synthesis strategy for determination of B-family vitamins. ACS Appl. Mater. Interfaces 2018, 10, 4140−4150. (31) Kim, Y. A.; Muramatsu, H.; Hayashi, T.; Endo, M.; Terrones, M.; Dresselhaus, M. S. Thermal stability and structural changes of double-walled carbon nanotubes by heat treatment. Chem. Phys. Lett. 2004, 398, 87−92. (32) Chang, B. Y. S.; Huang, N. M.; Nor An’amt, M. N.; Marlinda, A. R.; Norazriena, Y.; Muhamad, M. R.; Harrison, I.; Lim, H. N.; Chia, C. H. Facile hydrothermal preparation of titanium dioxide decorated reduced graphene oxide nanocomposite. Int. J. Nanomed. 2012, 7, 3379−3387. (33) Lehman, J. H.; Terrones, M.; Mansfield, E.; Hurst, K. E.; Meunier, V. Evaluating the characteristics of multiwall carbon nanotubes. Carbon 2011, 49, 2581−2602. (34) Lee, J.; Kim, M.; Hong, C. K.; Shim, S. E. Measurement of the dispersion stability of pristine and surface-modified multiwalled carbon nanotubes in various nonpolar and polar solvents. Meas. Sci. Technol. 2007, 18, 3707. (35) Peigney, A.; Laurent, C.; Flahaut, E.; Bacsa, R. R.; Rousset, A. Specific surface area of carbon nanotubes and bundles of carbon nanotubes. Carbon 2001, 39, 507−514. (36) Endo, M.; Hayashi, T.; Ahm Kim, Y.; Terrones, M.; Dresselhaus, M. S. Applications of carbon nanotubes in the twentyfirst century. Philos. Trans.: Math., Phys. Eng. Sci. Philos. Trans. R. Soc. London, Ser. A 2004, 362, 2223−2238. (37) Reid, C. R.; Thomas, K. M. Adsorption of gases on a carbon molecular sieve used for air separation: linear adsorptives as probes for kinetic selectivity. Langmuir 1999, 15, 3206−3218. (38) Agnihotri, S.; Mota, J. P. B.; Rostam-Abadi, M.; Rood, M. J. Structural characterization of single-walled carbon nanotube bundles by experiment and molecular simulation. Langmuir 2005, 21, 896− 904. (39) Pan, B.; Xing, B. Adsorption mechanisms of organic chemicals on carbon nanotubes. Environ. Sci. Technol. 2008, 42, 9005−9013. (40) Birch, M. E.; Ruda-Eberenz, T. A.; Chai, M.; Andrews, R.; Hatfield, R. L. Properties that influence the specific surface areas of carbon nanotubes and nanofibers. Ann Occup Hyg 2013, 57, 1148− 1166. (41) Zhu, W. Z.; Miser, D. E.; Chan, W. G.; Hajaligol, M. R. Characterization of multiwalled carbon nanotubes prepared by carbon arc cathode deposit. Mater. Chem. Phys. 2003, 82, 638−647. (42) Raymundo-Piñero, E.; Cazorla-Amorós, D.; Linares-Solano, A.; Delpeux, S.; Frackowiak, E.; Szostak, K.; Béguin, F. High surface area carbon nanotubes prepared by chemical activation. Carbon 2002, 40, 1614−1617. 858


ACS Omega

Article

(43) Condon, J. B. Surface Area and Porosity Determinations by Physisorption: Measurements and Theory; Elsevier, 2006. (44) Condon, J. B. Mesopore measurement by physical adsorption utilizing chi theory. Microporous Mesoporous Mater. 2005, 84, 105− 115. (45) Lowell, S.; Shields, J. E.; Thomas, M. A.; Thommes, M. Characterization of Porous Solids and Powders: Surface Area. Pore Size and Density; Springer Science & Business Media, 2012; Vol. 16. (46) Thomas, J. M.; Thomas, W. J. Principles and Practice of Heterogeneous Catalysis; John Wiley & Sons, 2014. (47) Brunauer, S.; Emmett, P. H.; Teller, E. Adsorption of Gases in Multimolecular Layers. J. Am. Chem. Soc. 1938, 60, 309−319. (48) Jagiello, J.; Thommes, M. Comparison of DFT characterization methods based on N 2 , Ar, CO 2 , and H 2 adsorption applied to carbons with various pore size distributions. Carbon 2004, 42, 1227− 1232. (49) Zaib, Q. Multiwalled Carbon Nanotubes/Titanium Dioxide for Water Treatment: Synthesis, Characterization, and Application; Masdar Institute of Science and Technology, Khalifa University: Abu Dhabi, UAE, 2016. (50) Taurozzi, J.; Hackley, V.; Wiesner, M. Preparation of Nanoparticle Dispersions from Powdered Material Using Ultrasonic Disruption; NIST Special Publication: 2012, Vol. 1200; p 2. (51) Maina, P.; Mbarawa, M. Investigating effects of zeolites as an agent to improve limestone reactivity toward flue gas desulfurization. Energy Fuels 2011, 25, 2028−2038. (52) Santos, H. M.; Lodeiro, C.; Capelo-Martínez, J.-L. The Power of Ultrasound. Ultrasound in Chemistry; Wiley-VCH Verlag GmbH & Co. KGaA, 2009; pp 1−16. (53) Anderson, M. J.; Whitcomb, P. J. RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments; Productivity press, 2005. (54) Arvidsson, R.; Kushnir, D.; Sandén, B. A.; Molander, S. Prospective Life Cycle Assessment of Graphene Production by Ultrasonication and Chemical Reduction. Environ. Sci. Technol. 2014, 48, 4529−4536.

859


Optimization of Carbon Nanotube Dispersions in ... - ACS Publications

Recommend Documents